List of explanations

Learning Support

Examples of research themes

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Examples of research themes

Research Achievements (Excerpts)

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Learning Support

List of explanations

In recent years, it has become commonplace for everyone to have a smartphone, and m-learning has become more familiar. Therefore, we are developing a learning support system that makes it easier to learn on a smartphone, more efficient, and easier to continue. In particular, in our laboratory, we are working on estimating the confidence level of a learner when answering a multiple-choice question, such as whether they understand the answer or answer based on intuition, examining the optimal timing for review notifications based on daily operation logs, and realizing other cutting-edge learning support using a variety of smart devices.

Examples of research themes

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

List of explanations

Learning Support

Examples of research themes

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Examples of research themes

Research Achievements (Excerpts)

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Learning Support

List of explanations

In recent years, it has become commonplace for everyone to have a smartphone, and m-learning has become more familiar. Therefore, we are developing a learning support system that makes it easier to learn on a smartphone, more efficient, and easier to continue. In particular, in our laboratory, we are working on estimating the confidence level of a learner when answering a multiple-choice question, such as whether they understand the answer or answer based on intuition, examining the optimal timing for review notifications based on daily operation logs, and realizing other cutting-edge learning support using a variety of smart devices.

Examples of research themes

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

List of explanations

Learning Support

Examples of research themes

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Examples of research themes

Research Achievements (Excerpts)

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Learning Support

List of explanations

In recent years, it has become commonplace for everyone to have a smartphone, and m-learning has become more familiar. Therefore, we are developing a learning support system that makes it easier to learn on a smartphone, more efficient, and easier to continue. In particular, in our laboratory, we are working on estimating the confidence level of a learner when answering a multiple-choice question, such as whether they understand the answer or answer based on intuition, examining the optimal timing for review notifications based on daily operation logs, and realizing other cutting-edge learning support using a variety of smart devices.

Examples of research themes

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

List of explanations

Learning Support

Examples of research themes

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Examples of research themes

Research Achievements (Excerpts)

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Learning Support

List of explanations

In recent years, it has become commonplace for everyone to have a smartphone, and m-learning has become more familiar. Therefore, we are developing a learning support system that makes it easier to learn on a smartphone, more efficient, and easier to continue. In particular, in our laboratory, we are working on estimating the confidence level of a learner when answering a multiple-choice question, such as whether they understand the answer or answer based on intuition, examining the optimal timing for review notifications based on daily operation logs, and realizing other cutting-edge learning support using a variety of smart devices.

Examples of research themes

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

List of explanations

Activity Recognition

We are also working on the development of learning support systems utilizing behavior recognition technology. In the following reference 2, we developed a method to determine the success or failure of touch typing by wearing glasses-type wearable devices during touch typing practice. In the following reference 3, we developed a method to detect the finger used for keystrokes by wearing a wristband-type wearable device during typing practice.

Confidence estimation

We are developing a method to estimate learner confidence, particularly for multiple-choice English vocabulary learning. In addition to the general characteristic that response times increase when learners are hesitant, we have improved the accuracy of confidence estimation by incorporating features measurable from electrooculography (EOG) using wearable devices worn during learning.

Recently, we have also been working on developing a person-dependent reduction method to mitigate individual differences and investigating the impact of learners' behavioral states during learning on learning efficiency.

Learning Support

Examples of research themes

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Examples of research themes

Research Achievements (Excerpts)

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Learning Support

List of explanations

In recent years, it has become commonplace for everyone to have a smartphone, and m-learning has become more familiar. Therefore, we are developing a learning support system that makes it easier to learn on a smartphone, more efficient, and easier to continue. In particular, in our laboratory, we are working on estimating the confidence level of a learner when answering a multiple-choice question, such as whether they understand the answer or answer based on intuition, examining the optimal timing for review notifications based on daily operation logs, and realizing other cutting-edge learning support using a variety of smart devices.

Examples of research themes

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Estimating the confidence level of respondents to multiple-choice questions, optimal notification timing in m-learning, AI/IoT teaching material development, etc.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

Research Achievements (Excerpts)

Touch-typing detection using eyewear: toward realizing a new interaction for typing applications, Sensors, 2019.
Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device, TENCON 2018, Jeju, Korea, 2018.
Stroked finger recognition using a wearable device while typing, CIIS 2019, Bangkok, Thailand, 2019.

List of explanations

Activity Recognition

We are also working on the development of learning support systems utilizing behavior recognition technology. In the following reference 2, we developed a method to determine the success or failure of touch typing by wearing glasses-type wearable devices during touch typing practice. In the following reference 3, we developed a method to detect the finger used for keystrokes by wearing a wristband-type wearable device during typing practice.

Confidence estimation

We are developing a method to estimate learner confidence, particularly for multiple-choice English vocabulary learning. In addition to the general characteristic that response times increase when learners are hesitant, we have improved the accuracy of confidence estimation by incorporating features measurable from electrooculography (EOG) using wearable devices worn during learning.

Recently, we have also been working on developing a person-dependent reduction method to mitigate individual differences and investigating the impact of learners' behavioral states during learning on learning efficiency.

Activity Recognition

We are also working on the development of learning support systems utilizing behavior recognition technology. In the following reference 2, we developed a method to determine the success or failure of touch typing by wearing glasses-type wearable devices during touch typing practice. In the following reference 3, we developed a method to detect the finger used for keystrokes by wearing a wristband-type wearable device during typing practice.

Confidence estimation

We are developing a method to estimate learner confidence, particularly for multiple-choice English vocabulary learning. In addition to the general characteristic that response times increase when learners are hesitant, we have improved the accuracy of confidence estimation by incorporating features measurable from electrooculography (EOG) using wearable devices worn during learning.

Recently, we have also been working on developing a person-dependent reduction method to mitigate individual differences and investigating the impact of learners' behavioral states during learning on learning efficiency.